Mind Matters Natural and Artificial Intelligence News and Analysis

Tagai-limits

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Big data analytics through machine learning, Artificial Intelligence concept background, Using deep learning algorithms for neural network data analysis, Abstract AI 3d illustration

Researchers: Is the Cost of Improving Deep Learning Sustainable?

At IEEE: System designers may have to go back to relying on experts again to tell them what matters, rather than on massive databases

Deep Learning is an approach to computer programming that attempts to mimic the human brain (artificial neural networks) so as to enable systems to cluster data and make accurate predictions (IBM). It’s the dominant AI system today, used to predict how proteins fold and analyse medical scans as well as to beat humans at Go. And yet, four Deep Learning researchers recently wrote in IEEE Spectrum that “The cost of improvement is becoming unsustainable.” As part of their special report, “The Great AI Reckoning,”they explain: While deep learning’s rise may have been meteoric, its future may be bumpy. Like Rosenblatt before them, today’s deep-learning researchers are nearing the frontier of what their tools can achieve. To understand why this will…

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Close-up image of coder typing on computer

The Search for the Universal Algorithm Continues

Why does machine learning always seem to be rounding a corner, only to eventually hit a wall?

DeepMind, a part of Alphabet (i.e., Google), has made many headlines in the past. The biggest was its development of AlphaGo, which used reinforcement learning to beat the number one Go player at the time (2017). DeepMind generalized this into AlphaZero, which is supposedly able to solve any two-player game of perfect information. DeepMind has come back into headlines recently with the attempt to build an AI which can generate any algorithm. While they are starting with map data, the goal is to generalize this and generate any desired algorithm. The search for such a “universal algorithm” has been essentially equivalent to the search for a perpetual motion machine in physics. The allure of both is obvious. In physics, if you…

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Robot eyes closeup

Sure, AI Could Run the World — Except for Its Fundamental Limits

But many of the basic errors, problems, and limitations have no easy solution

We are told that not only will AI take our jobs but it will take our bosses’ jobs and their bosses’ jobs and pretty soon., AI will be running the world… We can see those films on Netflix any night. Science writer and science fiction author Charles Q. Choi offers, in a longish piece at the Institute of Electrical and Electronic Engineers’ online magazine, Spectrum, talking about the real world where “Neural networks can be disastrously brittle, forgetful, and surprisingly bad at math.” AI frequently flubs and it is not clear how to make it flub less. Here are brief notes on three examples of the seven he offers: ➤“Brittle” 97% of AIs could not identify a school bus flipped…

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Robotic man cyborg face representing artificial intelligence 3D rendering

How To Flummox an AI Neural Network

Kids can figure out the same-different distinction. So can ducklings and bees. But top AI can't.

Science writer John Pavlus identifies a key limitation of artificial intelligence: The first episode of Sesame Street in 1969 included a segment called “One of These Things Is Not Like the Other.” Viewers were asked to consider a poster that displayed three 2s and one W, and to decide — while singing along to the game’s eponymous jingle — which symbol didn’t belong. Dozens of episodes of Sesame Street repeated the game, comparing everything from abstract patterns to plates of vegetables. Kids never had to relearn the rules. Understanding the distinction between “same” and “different” was enough. Machines have a much harder time. One of the most powerful classes of artificial intelligence systems, known as convolutional neural networks or CNNs,…

Shot of Corridor in Working Data Center Full of Rack Servers and Supercomputers with Pink Neon Visualization Projection of Data Transmission Through High Speed Internet.
Shot of Corridor in Working Data Center Full of Rack Servers and Supercomputers with Pink Neon Visualization Projection of Data Transmission Through High Speed Internet.

AI Researcher: Stop Calling Everything “Artificial Intelligence”

It’s not really intelligence, says Berkeley’s Michael Jordan, and we risk misunderstanding what these machines can really do for us

Computer scientist Michael I. Jordan, a leading AI researcher, says today’s artificial intelligence systems aren’t actually intelligent and people should stop talking about them as if they were: They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley. Katy Pretz, “Stop Calling Everything AI, Machine-Learning Pioneer Says” at IEEE Spectrum (March 31, 2031) Their principal role, he says, is to “augment human intelligence, via painstaking analysis of large…

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technician standing in front of giant circuitboard, slight zoom effect

For Computers, Smart Is Not the Same Thing as Fast

In response to a reader’s good question …

In a recent article, I argued that computers are not, and never can become smarter. An insightful reader wrote to ask, “What if smartness is defined by speed?” This is a good point. The debate revolves around the definition of “smart.” and if we define “smart” as “fast”, then since computers are certainly getting faster they will necessarily become smarter. Such a definition has intuitive appeal. Think of the world’s best chess player versus a beginner. One of the big distinctions is the chess expert will choose a good move more quickly than a beginner, and in general will play faster than a beginner. As such, play speed demonstrates a certain level of intelligence on the part of the player.…

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Shot of Corridor in Working Data Center Full of Rack Servers and Supercomputers with Pink Neon Visualization Projection of Data Transmission Through High Speed Internet.

Would Super AI Cure Cancer — or Destroy the Earth?

Max Planck Institute computer scientists say that we not only don’t but can’t know

An international team of computer scientists associated with the Max Planck Institute concluded that, given the nature of computers, there is no way of determining what superintelligent AI would do: An international team of computer scientists used theoretical calculations to show that it would be fundamentally impossible to control a super-intelligent AI “A super-intelligent machine that controls the world sounds like science fiction. But there are already machines that perform certain important tasks independently without programmers fully understanding how they learned it. The question therefore arises whether this could at some point become uncontrollable and dangerous for humanity”, says study co-author Manuel Cebrian, Leader of the Digital Mobilization Group at the Center for Humans and Machines, Max Planck Institute for…